Uncertainty in Artificial Intelligence
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A Hybrid Approach to Reasoning with Partially Elicited Preference Models
Vu Ha, Peter Haddawy
Classical Decision Theory provides a normative framework for representing and reasoning about complex preferences. Straightforward application of this theory to automate decision making is difficult due to high elicitation cost. In response to this problem, researchers have recently developed a number of qualitative, logic-oriented approaches for representing and reasoning about references. While effectively addressing some expressiveness issues, these logics have not proven powerful enough for building practical automated decision making systems. In this paper we present a hybrid approach to preference elicitation and decision making that is grounded in classical multi-attribute utility theory, but can make effective use of the expressive power of qualitative approaches. Specifically, assuming a partially specified multilinear utility function, we show how comparative statements about classes of decision alternatives can be used to further constrain the utility function and thus identify sup-optimal alternatives. This work demonstrates that quantitative and qualitative approaches can be synergistically integrated to provide effective and flexible decision support.
Keywords: Preference elicitation
Pages: 263-270
PS Link: http://cs.uwm.edu/~vu/research.htm
PDF Link: /papers/99/p263-ha.pdf
AUTHOR = "Vu Ha and Peter Haddawy",
TITLE = "A Hybrid Approach to Reasoning with Partially Elicited Preference Models",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "263--270"

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